Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography

TLDR

The study presents a flexible, cost‑effective, and accurate method to monitor landslides using a small UAV to collect aerial photography. The authors applied a Structure from Motion workflow to generate 3D models and DEMs from multi‑view UAV imagery, validated their geometric accuracy with ground control points, compared DEMs from July and November 2011 to analyze landslide dynamics, and used the COSI‑Corr image correlation technique to quantify and map terrain displacements. The method achieved horizontal accuracy of 7 cm and vertical accuracy of 6 cm, and the COSI‑Corr technique accurately mapped displacements of toes, soil chunks, and vegetation patches, though it could not capture scarp retreat, demonstrating that UAV‑based imagery combined with 3D reconstruction and image correlation offers flexible and effective tools for monitoring landslide dynamics.

Abstract

In this study, we present a flexible, cost-effective, and accurate method to monitor landslides using a small unmanned aerial vehicle (UAV) to collect aerial photography. In the first part, we apply a Structure from Motion (SfM) workflow to derive a 3D model of a landslide in southeast Tasmania from multi-view UAV photography. The geometric accuracy of the 3D model and resulting DEMs and orthophoto mosaics was tested with ground control points coordinated with geodetic GPS receivers. A horizontal accuracy of 7 cm and vertical accuracy of 6 cm was achieved. In the second part, two DEMs and orthophoto mosaics acquired on 16 July 2011 and 10 November 2011 were compared to study landslide dynamics. The COSI-Corr image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two hillshaded DEM layers corresponded to a visual interpretation of landslide change. Results show that the algorithm can accurately map displacements of the toes, chunks of soil, and vegetation patches on top of the landslide, but is not capable of mapping the retreat of the main scarp. The conclusion is that UAV-based imagery in combination with 3D scene reconstruction and image correlation algorithms provide flexible and effective tools to map and monitor landslide dynamics.